@ KelAI
Job description: Founding Machine Learning Engineer
About KelAI KelAI turns market noise into systematic alpha. We are building an AI market intelligence engine that generates systematic financial insights to empower quantitative research and strategy development for asset management teams. Built by a quant veteran from a top tier firm, KelAI is focused on building purpose-built AI applications for systematic finance, shaping research infrastructure for the next generation of asset management workflows. KelAI is backed by Y Combinator.
The Role We are looking for a Founding Machine Learning Engineer to lead the expansion of the core intelligence layer of KelAI. This is a high-ownership role for someone with deep machine learning experience who wants to work at the intersection of AI, financial markets, and systematic investing. You will help design and build systems that generate financial insights, support quantitative research, and accelerate strategy development for institutional asset management teams. As an early technical hire, you will have meaningful influence over KelAI's technical architecture, model strategy, product direction, and engineering culture.
What You'll Do • Build machine learning systems that generate systematic financial insights from market-relevant data • Develop AI workflows that support quantitative research, signal exploration, and strategy development • Design evaluation frameworks for insight quality, statistical relevance, robustness, and research usefulness • Work closely with the founding team to translate asset management workflows into scalable technical systems • Build data, model, retrieval, ranking, and reasoning pipelines for financial research use cases • Fine tune LLMs, build retrieval systems and agentic research workflows • Help define KelAI's technical foundation, product roadmap, and engineering standards from the ground up
What We're Looking For • 3-5+ years of hands-on machine learning experience • Deep ML experience at a top-tier technology company, AI lab, or quantitative finance firm (in an ML / research engineering capacity) • Strong engineering ability and comfort building enterprise-grade systems • Strong Python skills and experience with ML frameworks such as PyTorch, JAX, TensorFlow or Hugging Face • Experience with open source model fine-tuning, evaluation, deployment, monitoring, and iteration • Experience working with large-scale data systems, retrieval, ranking, LLM applications, or quantitative research infrastructure • Strong analytical judgment and ability to reason about model outputs, data quality, and signal validity • High ownership, speed, and comfort operating in an early-stage company • Interest in financial markets, systematic investing, and AI-native investment research
Especially Valuable • ML / research engineering experience at firms such as Meta, Google, OpenAI, Anthropic, or at the ML teams within Jane Street, Citadel, HRT, Two Sigma, or DE Shaw (ML roles specifically — quant trader and PM tracks are not the target) • Background in quantitative research, systematic investing, financial data, or investment technology • Experience building LLM-powered research tools, RAG systems, agentic workflows, or financial NLP systems • Familiarity with alpha research, backtesting, factor models, portfolio construction, or investment strategy development • Prior startup, founding engineer, or zero-to-one product experience
Why Join KelAI • Founding-level ownership over a technically ambitious AI product • Build at the frontier of AI, financial intelligence, and systematic research • Direct influence over product, architecture, hiring, and company direction • Meaningful equity and the opportunity to help define a category from the earliest stage
Location New York City preferred; hybrid flexibility for exceptional candidates
Compensation $250k–$350k total compensation, calibrated to experience and prior firm